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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

SectionObjectives
Databricks Lakehouse Platform Fundamentals- Workspace, architecture, and core platform concepts
- Clusters, notebooks, and basic Databricks environment usage
Productionizing Data Pipelines- Databricks Workflows / Jobs orchestration
- Pipeline deployment and operationalization
- Scheduling and monitoring jobs
Data Processing and Transformations- User-defined functions (UDFs)
- Delta Lake fundamentals (tables, transactions, optimization)
- Apache Spark SQL operations (joins, aggregations, filtering)
- PySpark DataFrame transformations
Data Ingestion and ELT Development- Handling structured and semi-structured data
- Data ingestion using Spark SQL and PySpark
- ETL patterns and transformations
Data Governance and Quality- Unity Catalog basics
- Data access control and governance
- Data quality concepts and management

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q180-Q185):

NEW QUESTION # 180
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

Answer: A

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. Reference:
Create a serverless SQL endpoint
Assign a SQL endpoint to a dashboard
SQL Analytics API


NEW QUESTION # 181
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: D

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)


NEW QUESTION # 182
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query.
For the first week following the project's release, the managerwants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

Answer: A


NEW QUESTION # 183
A pipeline uses COPY INTO to ingest CSV files from cloud object storage into a Unity Catalog Delta table. Some files are occasionally re-uploaded with corrections using the same filename.
The engineer needs the corrected data ingested as soon as it occurs. What should the engineer do?

Answer: B

Explanation:
COPY INTO skips files it has already loaded, even when a file is modified and re-uploaded under the same name. Corrections should arrive as new files and then be applied to the target with update logic such as MERGE.


NEW QUESTION # 184
What are the transformations typically included in building the Bronze layer ?

Answer: C

Explanation:
In the Medallion Architecture promoted by Databricks, the Bronze layer represents the raw ingestion layer where data is stored in its original form with minimal transformation. The primary goal is to ensure data fidelity and traceability , not to apply business logic or heavy transformations. Typical operations in the Bronze layer include schema inference (or enforcement), ingestion of raw data, and the addition of metadata columns such as ingestion timestamp, load date/time, and process identifiers. These metadata fields help track lineage, enable debugging, and support incremental processing downstream. Extensive data cleansing (A), aggregations (B), and business transformations (C) are intentionally deferred to the Silver and Gold layers , where data is refined and structured for analytics. By keeping Bronze data close to the source and append-only, organizations maintain a reliable audit trail and can reprocess data if needed. Therefore, adding operational metadata like load timestamps and process IDs is the correct characteristic of Bronze layer transformations.
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NEW QUESTION # 185
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